127 citations · 221 across the 6 of their papers we have counts for
7 papers
Taming Detection Transformers for Medical Object Detection
Marc K. Ickler, Michael Baumgartner, Saikat Roy +2
The accurate detection of suspicious regions in medical images is an error-prone and time-consuming process required by many routinely performed diagnostic procedures. To support c…
atTRACTive: Semi-automatic white matter tract segmentation using active learning
Robin Peretzke, Klaus Maier-Hein, Jonas Bohn +6
Accurately identifying white matter tracts in medical images is essential for various applications, including surgery planning and tract-specific analysis. Supervised machine learn…
SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model
Saikat Roy, Tassilo Wald, Gregor Koehler +5
Foundation models have taken over natural language processing and image generation domains due to the flexibility of prompting. With the recent introduction of the Segment Anything…
Transformer Utilization in Medical Image Segmentation Networks
Saikat Roy, Gregor Koehler, Michael Baumgartner +4
Owing to success in the data-rich domain of natural images, Transformers have recently become popular in medical image segmentation. However, the pairing of Transformers with convo…
MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation
Saikat Roy, Gregor Koehler, Constantin Ulrich +5
There has been exploding interest in embracing Transformer-based architectures for medical image segmentation. However, the lack of large-scale annotated medical datasets make achi…
Handwritten Isolated Bangla Compound Character Recognition: a new benchmark using a novel deep learning approach
Saikat Roy, Nibaran Das, Mahantapas Kundu +1
In this work, a novel deep learning technique for the recognition of handwritten Bangla isolated compound character is presented and a new benchmark of recognition accuracy on the…
Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks
Arindam Das, Saikat Roy, Ujjwal Bhattacharya +1
In this work, a region-based Deep Convolutional Neural Network framework is proposed for document structure learning. The contribution of this work involves efficient training of r…